本文实例为大家分享了基于numpy实现逻辑回归的具体代码，供大家参考，具体内容如下
交叉熵损失函数；sigmoid激励函数
基于numpy的逻辑回归的程序如下：
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import numpy as npimport matplotlib.pyplot as pltfrom sklearn.datasets.samples_generator import make_classificationclass logistic_regression(): def __init__(self): pass def sigmoid(self, x): z = 1 /(1 + np.exp(-x)) return z def initialize_params(self, dims): W = np.zeros((dims, 1)) b = 0 return W, b def logistic(self, X, y, W, b): num_train = X.shape[0] num_feature = X.shape[1] a = self.sigmoid(np.dot(X, W) + b) cost = -1 / num_train * np.sum(y * np.log(a) + (1 - y) * np.log(1 - a)) dW = np.dot(X.T, (a - y)) / num_train db = np.sum(a - y) / num_train cost = np.squeeze(cost)#[]列向量，易于plot return a, cost, dW, db def logistic_train(self, X, y, learning_rate, epochs): W, b = self.initialize_params(X.shape[1]) cost_list = [] for i in range(epochs): a, cost, dW, db = self.logistic(X, y, W, b) W = W - learning_rate * dW b = b - learning_rate * db if i % 100 == 0: cost_list.append(cost) if i % 100 == 0: print('epoch %d cost %f' % (i, cost)) params = { 'W': W, 'b': b } grads = { 'dW': dW, 'db': db } return cost_list, params, grads def predict(self, X, params): y_prediction = self.sigmoid(np.dot(X, params['W']) + params['b']) #二分类 for i in range(len(y_prediction)): if y_prediction[i] > 0.5: y_prediction[i] = 1 else: y_prediction[i] = 0 return y_prediction #精确度计算 def accuracy(self, y_test, y_pred): correct_count = 0 for i in range(len(y_test)): for j in range(len(y_pred)): if y_test[i] == y_pred[j] and i == j: correct_count += 1 accuracy_score = correct_count / len(y_test) return accuracy_score #创建数据 def create_data(self): X, labels = make_classification(n_samples=100, n_features=2, n_redundant=0, n_informative=2) labels = labels.reshape((-1, 1)) offset = int(X.shape[0] * 0.9) #训练集与测试集的划分 X_train, y_train = X[:offset], labels[:offset] X_test, y_test = X[offset:], labels[offset:] return X_train, y_train, X_test, y_test #画图函数 def plot_logistic(self, X_train, y_train, params): n = X_train.shape[0] xcord1 = [] ycord1 = [] xcord2 = [] ycord2 = [] for i in range(n): if y_train[i] == 1:#1类 xcord1.append(X_train[i][0]) ycord1.append(X_train[i][1]) else:#0类 xcord2.append(X_train[i][0]) ycord2.append(X_train[i][1]) fig = plt.figure() ax = fig.add_subplot(111) ax.scatter(xcord1, ycord1, s=32, c='red') ax.scatter(xcord2, ycord2, s=32, c='green')#画点 x = np.arange(-1.5, 3, 0.1) y = (-params['b'] - params['W'][0] * x) / params['W'][1]#画二分类直线 ax.plot(x, y) plt.xlabel('X1') plt.ylabel('X2') plt.show()if __name__ == "__main__": model = logistic_regression() X_train, y_train, X_test, y_test = model.create_data() print(X_train.shape, y_train.shape, X_test.shape, y_test.shape) # (90, 2)(90, 1)(10, 2)(10, 1) #训练模型 cost_list, params, grads = model.logistic_train(X_train, y_train, 0.01, 1000) print(params) #计算精确度 y_train_pred = model.predict(X_train, params) accuracy_score_train = model.accuracy(y_train, y_train_pred) print('train accuracy is:', accuracy_score_train) y_test_pred = model.predict(X_test, params) accuracy_score_test = model.accuracy(y_test, y_test_pred) print('test accuracy is:', accuracy_score_test) model.plot_logistic(X_train, y_train, params) |
结果如下所示：
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/exsolar_521/article/details/108206644
